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    Select Rows From List of Values in Pandas DataFrame

    Select Rows From List of Values in Pandas DataFrame

    To select rows from a Pandas DataFrame based on a list of values, you can use the isin() method. You can select rows from a list of values in pandas DataFrame either using DataFrame.isin(), DataFrame.query(), DataFrame.index(), DataFrame.loc[] attribute or DataFrame.apply() method with a lambda function. In this article, I will explain how to use a list of values to select rows from Pandas DataFrame with examples.

    Key Points –

    Related:

    <a id="h-quick-examples-of-select-dataframe-rows-from-list-of-values-in-pandas"></a>

    Quick Examples of Select DataFrame Rows From List of Values in Pandas

    If you are in a hurry, below are some quick examples of how to use a list of values to select rows from Pandas DataFrame.

    
# Quick examples of select dataframe rows from list of values 

# Example 1: Create a list of values for select rows 
# Using isin([]) method
list_of_values = [25000, 30000]
df2 = df[df['Fee'].isin(list_of_values)]

# Example 2: Using isin([]) method 
# To get opposite rows
df2 = df[~df['Fee'].isin([25000, 30000])]

# Example 3: Using DataFrame.query() method 
# To select rows
df2 = df.query('Discount in [1000,2000]')

# Example 4: Filter rows by list of values
df2 = df.query('Discount == [1000,2000]')

# Example 5: Select list of rows using variable
list_of_value = [1000,2000]
df2 = df.query('Discount in @list_of_value')

# Example 6: Using DataFrame.query() method
list_of_value = [1000,2000]
df2 = df.query('Discount == @list_of_value')

# Example 7: Select rows using DataFrame.loc[] 
# To apply() and lambda function 
df2 = df.loc[df.apply(lambda x: x.Fee in [20000,22000], axis=1)]

# Example 8: Select pandas rows based on list index
index_list = [0,2]
df2 = df.loc[df.index[index_list]]

# Example 9: Using DataFrame.iloc[] 
# To select rows list index
index_list = [0,2]
df2 = df.iloc[index_list]

    Now, let's create a DataFrame with a few rows and columns, execute these examples and validate results. Our DataFrame contains column names Courses, Fee, Duration, and Discount.

    
# Create DataFrame
import pandas as pd

technologies = {
    'Courses':["Spark","PySpark","Python","pandas"],
    'Fee' :[20000,25000,22000,30000],
    'Duration':['30days','40days','35days','50days'],
    'Discount':[1000,2300,1200,2000]
              }
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print("Create DataFrame\n",df)

    Yields below output.

    <a id="h-using-dataframe-isin-to-select-rows-from-list-of-values"></a>

    Using DataFrame.isin() to Select Rows From List of Values

    DataFrame.isin() method is used to filter/select rows from a list of values. You can have the list of values in variable and use it on isin() or use it directly.

    This program creates a boolean mask using isin() on the 'Fee' column and then uses this mask to select the rows where the 'Fee' values match any value in the list_of_values.

    
# Create a list of values for select rows 
# Using isin([]) method
list_of_values = [25000, 30000]
df2 = df[df['Fee'].isin(list_of_values)]
print("Selecting rows from the list of values:\n",df2)

    Yields below output.

    Alternatively, you can use the tilde (~) to get the opposite rows. The tilde is used as a bitwise NOT operator, which in this context is used to invert the boolean mask created by isin([]).

    
# Using isin([]) method to get opposite rows
df2 = df[~df['Fee'].isin([25000, 30000])]
print(df2)

    Yields below output.

    
# Output:
   Courses    Fee Duration  Discount
r1   Spark  20000   30days      1000
r3  Python  22000   35days      1200

    <a id="h-using-dataframe-query-method-to-select-multiple-rows-values"></a>

    Using DataFrame.query() Method to Select Multiple Rows Values

    Also, use pandas.DataFrame.query() to filter Pandas DataFrame rows from a list of multiple values. With this, you can use in and == operators. Select the rows where the 'Discount' column values are either 1000 or 2000. The query() method allows you to write conditions in a SQL-like syntax for convenient DataFrame filtering.

    
# Using DataFrame.query() method to select rows
df2 = df.query('Discount in [1000,2000]')
print(df2)

# Filter Rows by list of values
df2 = df.query('Discount == [1000,2000]')
print(df2)

# Select list of rows using variable
list_of_value = [1000,2000]
df2 = df.query('Discount in @list_of_value')
print(df2)

# Using DataFrame.query() method
list_of_value = [1000,2000]
df2 = df.query('Discount == @list_of_value')
print(df2)

    Yields below output.

    
# Output:
   Courses    Fee Duration  Discount
r1   Spark  20000   30days      1000
r4  pandas  30000   50days      2000

    <a id="h-select-rows-using-dataframe-loc-with-apply-and-lambda"></a>

    Select Rows Using DataFrame.loc[] with apply() and Lambda

    Use DataFrame.loc[] along with apply() and lambda function to select rows that are matching in a list. To select the rows where the 'Fee' column values are either 20000 or 22000. The apply() function with a lambda is used to create a boolean mask, and DataFrame.loc[] is used to select the rows based on this mask.

    
# Select rows using DataFrame.loc[] to apply() and lambda function 
df2 = df.loc[df.apply(lambda x: x.Fee in [20000,22000], axis=1)]
print("Selecting rows from the list of values:\n",df2)

    Yields below output.

    
# Output:
Selecting rows from the list of values:
   Courses    Fee Duration  Discount
r1   Spark  20000   30days      1000
r3  Python  22000   35days      1200

    <a id="h-select-pandas-rows-based-on-list-index"></a>

    Select Pandas Rows Based on List Index

    If you want to select specific rows from a Pandas DataFrame based on a list of indices, you can use the loc[] or iloc[] indexing methods. You can use loc[] or iloc[] with your desired indices to select rows from DataFrame. For more details refer to Using Index with loc[] to select rows from DataFrame.

    Both loc and iloc allow you to select rows based on specific indices, but they differ in how they interpret the indices (label-based for loc and integer-based for iloc). Choose the one that best fits your needs.

    
# Select pandas rows based on list index
index_list = [0,2]
df2 = df.loc[df.index[index_list]]
print("Selected rows based on list of indices:\n",df2)

# Using DataFrame.iloc[] 
# To select rows list index
index_list = [0,2]
df2 = df.iloc[index_list]
print("Selected rows based on list of indices:\n",df2)

    Yields below output.

    
# Output:
Selected rows based on list of indices:
  Courses    Fee Duration  Discount
0   Spark  20000   30days      1000
2  Python  22000   35days      1200

    <a id="h-complete-example-to-select-rows-from-list-of-values-in-dataframe"></a>

    Complete Example to Select Rows From List of Values in DataFrame

    
import pandas as pd
technologies = {
    'Courses':["Spark","PySpark","Python","pandas"],
    'Fee' :[20000,25000,22000,30000],
    'Duration':['30days','40days','35days','50days'],
    'Discount':[1000,2300,1200,2000]
              }
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
df2 = df[df['Duration'].str.contains('35days')]
print(df2)

# Create a list of values for select rows 
# Using isin([]) method
list_of_values = [25000, 30000]
df2 = df[df['Fee'].isin(list_of_values)]
print(df2)

# Using isin([]) method to get opposite rows
df2 = df[~df['Fee'].isin([25000, 30000])]
print(df2)

# Using DataFrame.query() method to select rows
df2 = df.query('Discount in [1000,2000]')
print(df2)

# Filter Rows by list of values
df2 = df.query('Discount == [1000,2000]')
print(df2)

# Select list of rows using variable
list_of_value = [1000,2000]
df2 = df.query('Discount in @list_of_value')
print(df2)

# Using DataFrame.query() method
list_of_value = [1000,2000]
df2 = df.query('Discount == @list_of_value')
print(df2)

# Select rows using DataFrame.loc[] to apply() and lambda function 
df2 = df.loc[df.apply(lambda x: x.Fee in [20000,22000], axis=1)]
print(df2)

# Select pandas rows based on list index
index_list = [0,2]
df2 = df.loc[df.index[index_list]]
print(df2)

# Using DataFrame.iloc[] 
# To select rows list index
index_list = [0,2]
df2 = df.iloc[index_list]
print(df2)

    <a id="h-faq-on-select-rows-from-list-of-values-in-pandas-dataframe"></a>

    FAQ on Select Rows From List of Values in Pandas DataFrame

    You can select rows from a Pandas DataFrame based on multiple conditions by combining the conditions using logical operators like & (AND) and | (OR). For example, the conditions are combined using the & operator. You can adjust the conditions to match your specific criteria. If you want to use the OR operator, you can use |.

    To select rows based on multiple values in one column of a Pandas DataFrame, you can use the isin method.

    You can use the query() method in Pandas to select rows based on a condition. The query() method allows you to filter rows using a query expression in a SQL-like syntax.

    To select rows based on a list of indices in a Pandas DataFrame, you can use the loc or iloc method. For example, df.loc[selected_indices] is used to select rows based on the specified list of indices. The loc method is label-based, so it takes the actual index labels of the DataFrame.

    You can select rows based on a custom function applied to a column in a Pandas DataFrame. You can use the apply() method along with a lambda function or a custom function.

    <a id="h-conclusion"></a>

    Conclusion

    In this article, you have learned how to use a list of values to select rows from DataFrame using DataFrame.isin(), DataFrame.query() and DataFrame.index() functions. Also, you have learned to select rows with row indices.

    Happy Learning !!

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